arXiv:2601.09716cs.CL2026-01

首份系统梳理塞内加尔六种官方语言NLP进展与挑战的报告

Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research

  • 整合语言、技术与基础设施因素,分析六种语言数字可用性
  • 揭示数据、工具与基准缺失问题,推动资源集中共享
  • 聚焦社科应用,助力田野研究高效包容

自然语言处理(NLP)正快速改变各学科的研究方法,但非洲语言在这一技术变革中仍严重缺位。本文首次全面概述塞内加尔宪法承认的六种官方语言——沃洛夫语、富拉语、塞尔雷语、迪奥拉语、曼丁语和索宁克语——在NLP领域的进展与挑战。我们综合分析了语言、社会技术与基础设施因素对这些语言数字准备度的影响,识别出数据、工具与基准的缺口。基于现有倡议与研究成果,本文梳理了涵盖文本与语音模态的各类任务进展,并建立一个集中式GitHub仓库,整合公开可获取的多任务NLP资源,以促进协作与可复现性。特别关注NLP在社会科学中的应用,强调多语言转录、翻译与检索流水线能显著提升田野研究的效率与包容性。最后,论文提出可持续、以社区为中心的塞内加尔语言NLP生态发展路线图,强调伦理数据治理、开放资源及跨学科合作。

原文摘要 · Abstract (English)

Natural Language Processing (NLP) is rapidly transforming research methodologies across disciplines, yet African languages remain largely underrepresented in this technological shift. This paper provides the first comprehensive overview of NLP progress and challenges for the six national languages officially recognized by the Senegalese Constitution: Wolof, Pulaar, Sérère, Diola, Mandingue, and Soninké. We synthesize linguistic, socio-technical, and infrastructural factors that shape their digital readiness and identify gaps in data, tools, and benchmarks. Building on existing initiatives and research works, we analyze ongoing efforts in various tasks, covering both text and speech modalities. We also provide a centralized GitHub repository that compiles publicly accessible resources for a range of NLP tasks across these languages, designed to facilitate collaboration and reproducibility. A special focus is devoted to the application of NLP to the social sciences, where multilingual transcription, translation, and retrieval pipelines can significantly enhance the efficiency and inclusiveness of field research. The paper concludes by outlining a roadmap toward sustainable, community-centered NLP ecosystems for Senegalese languages, emphasizing ethical data governance, open resources, and interdisciplinary collaboration.

NLP低资源语言非洲语言社会科学研究

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